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   "source": [
    "# How many cases of COVID-19 does each U.S. state really have?\n",
    "> Reported U.S. case counts are based on the number of administered tests. Since not everyone is tested, this number is biased. We use Bayesian techniques to estimate the true number of cases.\n",
    "\n",
    "- author: Joseph Richards\n",
    "- image: images/covid-state-case-estimation.png\n",
    "- hide: false\n",
    "- comments: true\n",
    "- categories: [MCMC, US, states, cases]\n",
    "- permalink: /covid-19-us-case-estimation/\n",
    "- toc: false"
   ]
  },
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   "source": [
    "> Warning: This analysis contains the results of a predictive model. There are a number of assumptions made which include some speculation.  Furthermore, this analysis was not prepared or reviewed by an Epidimiologist. Therefore, the assumptions and methods presented should be scrutinized carefully before arriving at any conclusions. "
   ]
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      "WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
     ]
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   "source": [
    "#hide\n",
    "\n",
    "# Setup and imports\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymc3 as pm\n",
    "import requests\n",
    "\n",
    "from IPython.display import display, Markdown"
   ]
  },
  {
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   "source": [
    "#hide\n",
    "\n",
    "# Data utilities:\n",
    "\n",
    "def get_statewise_testing_data():\n",
    "    '''\n",
    "    Pull all statewise data required for model fitting and\n",
    "    prediction\n",
    "\n",
    "    Returns:\n",
    "    * df_out: DataFrame for model fitting where inclusion\n",
    "        requires testing data from 7 days ago\n",
    "    * df_pred: DataFrame for count prediction where inclusion\n",
    "        only requires testing data from today\n",
    "    '''\n",
    "\n",
    "    # Pull testing counts by state:\n",
    "    out = requests.get('https://covidtracking.com/api/states')\n",
    "    df_out = pd.DataFrame(out.json())\n",
    "    df_out.set_index('state', drop=True, inplace=True)\n",
    "\n",
    "    # Pull time-series of testing counts:\n",
    "    ts = requests.get('https://covidtracking.com/api/states/daily')\n",
    "    df_ts = pd.DataFrame(ts.json())\n",
    "\n",
    "    # Get data from last week\n",
    "    date_last_week = df_ts['date'].unique()[7]\n",
    "    df_ts_last_week = _get_test_counts(df_ts, df_out.index, date_last_week)\n",
    "    df_out['num_tests_7_days_ago'] = \\\n",
    "        (df_ts_last_week['positive'] + df_ts_last_week['negative'])\n",
    "    df_out['num_pos_7_days_ago'] = df_ts_last_week['positive']\n",
    "\n",
    "    # Get data from today:\n",
    "    df_out['num_tests_today'] = (df_out['positive'] + df_out['negative'])\n",
    "\n",
    "    # State population:\n",
    "    df_pop = pd.read_excel(('https://github.com/jwrichar/COVID19-mortality/blob/'\n",
    "                            'master/data/us_population_by_state_2019.xlsx?raw=true'),\n",
    "                           skiprows=2, skipfooter=5)\n",
    "    r = requests.get(('https://raw.githubusercontent.com/jwrichar/COVID19-mortality/'\n",
    "                      'master/data/us-state-name-abbr.json'))\n",
    "    state_name_abbr_lookup = r.json()\n",
    "    \n",
    "    df_pop.index = df_pop['Geographic Area'].apply(\n",
    "        lambda x: str(x).replace('.', '')).map(state_name_abbr_lookup)\n",
    "    df_pop = df_pop.loc[df_pop.index.dropna()]\n",
    "\n",
    "    df_out['total_population'] = df_pop['Total Resident\\nPopulation']\n",
    "\n",
    "    # Tests per million people, based on today's test coverage\n",
    "    df_out['tests_per_million'] = 1e6 * \\\n",
    "        (df_out['num_tests_today']) / df_out['total_population']\n",
    "    df_out['tests_per_million_7_days_ago'] = 1e6 * \\\n",
    "        (df_out['num_tests_7_days_ago']) / df_out['total_population']\n",
    "\n",
    "    # People per test:\n",
    "    df_out['people_per_test'] = 1e6 / df_out['tests_per_million']\n",
    "    df_out['people_per_test_7_days_ago'] = \\\n",
    "        1e6 / df_out['tests_per_million_7_days_ago']\n",
    "\n",
    "    # Drop states with messed up / missing data:\n",
    "    # Drop states with missing total pop:\n",
    "    to_drop_idx = df_out.index[df_out['total_population'].isnull()]\n",
    "    print('Dropping %i/%i states due to lack of population data: %s' %\n",
    "          (len(to_drop_idx), len(df_out), ', '.join(to_drop_idx)))\n",
    "    df_out.drop(to_drop_idx, axis=0, inplace=True)\n",
    "\n",
    "    df_pred = df_out.copy(deep=True)  # Prediction DataFrame\n",
    "\n",
    "    # Criteria for model fitting:\n",
    "    # Drop states with missing test count 7 days ago:\n",
    "    to_drop_idx = df_out.index[df_out['num_tests_7_days_ago'].isnull()]\n",
    "    print('Dropping %i/%i states due to lack of tests: %s' %\n",
    "          (len(to_drop_idx), len(df_out), ', '.join(to_drop_idx)))\n",
    "    df_out.drop(to_drop_idx, axis=0, inplace=True)\n",
    "    # Drop states with no cases 7 days ago:\n",
    "    to_drop_idx = df_out.index[df_out['num_pos_7_days_ago'] == 0]\n",
    "    print('Dropping %i/%i states due to lack of positive tests: %s' %\n",
    "          (len(to_drop_idx), len(df_out), ', '.join(to_drop_idx)))\n",
    "    df_out.drop(to_drop_idx, axis=0, inplace=True)\n",
    "\n",
    "    # Criteria for model prediction:\n",
    "    # Drop states with missing test count today:\n",
    "    to_drop_idx = df_pred.index[df_pred['num_tests_today'].isnull()]\n",
    "    print('Dropping %i/%i states in prediction data due to lack of tests: %s' %\n",
    "          (len(to_drop_idx), len(df_pred), ', '.join(to_drop_idx)))\n",
    "    df_pred.drop(to_drop_idx, axis=0, inplace=True)\n",
    "    # Cast counts to int\n",
    "    df_pred['negative'] = df_pred['negative'].astype(int)\n",
    "    df_pred['positive'] = df_pred['positive'].astype(int)\n",
    "\n",
    "    return df_out, df_pred\n",
    "\n",
    "def _get_test_counts(df_ts, state_list, date):\n",
    "\n",
    "    ts_list = []\n",
    "    for state in state_list:\n",
    "        state_ts = df_ts.loc[df_ts['state'] == state]\n",
    "        # Back-fill any gaps to avoid crap data gaps\n",
    "        state_ts.fillna(method='bfill', inplace=True)\n",
    "\n",
    "        record = state_ts.loc[df_ts['date'] == date]\n",
    "        ts_list.append(record)\n",
    "\n",
    "    df_ts = pd.concat(ts_list, ignore_index=True)\n",
    "    return df_ts.set_index('state', drop=True)\n"
   ]
  },
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   "source": [
    "#hide\n",
    "\n",
    "# Model utilities\n",
    "\n",
    "def case_count_model_us_states(df):\n",
    "\n",
    "    # Normalize inputs in a way that is sensible:\n",
    "\n",
    "    # People per test: normalize to South Korea\n",
    "    # assuming S.K. testing is \"saturated\"\n",
    "    ppt_sk = np.log10(51500000. / 250000)\n",
    "    df['people_per_test_normalized'] = (\n",
    "        np.log10(df['people_per_test_7_days_ago']) - ppt_sk)\n",
    "\n",
    "    n = len(df)\n",
    "\n",
    "    # For each country, let:\n",
    "    # c_obs = number of observed cases\n",
    "    c_obs = df['num_pos_7_days_ago'].values\n",
    "    # c_star = number of true cases\n",
    "\n",
    "    # d_obs = number of observed deaths\n",
    "    d_obs = df[['death', 'num_pos_7_days_ago']].min(axis=1).values\n",
    "    # people per test\n",
    "    people_per_test = df['people_per_test_normalized'].values\n",
    "\n",
    "    covid_case_count_model = pm.Model()\n",
    "\n",
    "    with covid_case_count_model:\n",
    "\n",
    "        # Priors:\n",
    "        mu_0 = pm.Beta('mu_0', alpha=1, beta=100, testval=0.01)\n",
    "        # sig_0 = pm.Uniform('sig_0', lower=0.0, upper=mu_0 * (1 - mu_0))\n",
    "        alpha = pm.Bound(pm.Normal, lower=0.0)(\n",
    "            'alpha', mu=8, sigma=3, shape=1)\n",
    "        beta = pm.Bound(pm.Normal, upper=0.0)(\n",
    "            'beta', mu=-1, sigma=1, shape=1)\n",
    "        # beta = pm.Normal('beta', mu=0, sigma=1, shape=3)\n",
    "        sigma = pm.HalfNormal('sigma', sigma=0.5, testval=0.1)\n",
    "        # sigma_1 = pm.HalfNormal('sigma_1', sigma=2, testval=0.1)\n",
    "\n",
    "        # Model probability of case under-reporting as logistic regression:\n",
    "        mu_model_logit = alpha + beta * people_per_test\n",
    "        tau_logit = pm.Normal('tau_logit',\n",
    "                              mu=mu_model_logit,\n",
    "                              sigma=sigma,\n",
    "                              shape=n)\n",
    "        tau = np.exp(tau_logit) / (np.exp(tau_logit) + 1)\n",
    "\n",
    "        c_star = c_obs / tau\n",
    "\n",
    "        # Binomial likelihood:\n",
    "        d = pm.Binomial('d',\n",
    "                        n=c_star,\n",
    "                        p=mu_0,\n",
    "                        observed=d_obs)\n",
    "\n",
    "    return covid_case_count_model"
   ]
  },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping 4/56 states due to lack of population data: AS, GU, MP, VI\n",
      "Dropping 0/52 states due to lack of tests: \n",
      "Dropping 0/52 states due to lack of positive tests: \n",
      "Dropping 0/52 states in prediction data due to lack of tests: \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sequential sampling (1 chains in 1 job)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NUTS: [tau_logit, sigma, beta, alpha, mu_0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
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      "Sampling chain 0, 0 divergences:  99%|█████████▉| 993/1000 [02:09<00:01,  6.55it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Sampling chain 0, 0 divergences:  99%|█████████▉| 994/1000 [02:09<00:00,  6.59it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Sampling chain 0, 0 divergences: 100%|█████████▉| 995/1000 [02:10<00:00,  6.49it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Sampling chain 0, 0 divergences: 100%|█████████▉| 996/1000 [02:10<00:00,  6.20it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Sampling chain 0, 0 divergences: 100%|█████████▉| 997/1000 [02:10<00:00,  6.16it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Sampling chain 0, 0 divergences: 100%|█████████▉| 998/1000 [02:10<00:00,  6.25it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Sampling chain 0, 0 divergences: 100%|█████████▉| 999/1000 [02:10<00:00,  6.36it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Sampling chain 0, 0 divergences: 100%|██████████| 1000/1000 [02:10<00:00,  6.48it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Sampling chain 0, 0 divergences: 100%|██████████| 1000/1000 [02:10<00:00,  7.64it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "The chain reached the maximum tree depth. Increase max_treedepth, increase target_accept or reparameterize.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Only one chain was sampled, this makes it impossible to run some convergence checks\n"
     ]
    }
   ],
   "source": [
    "#hide\n",
    "\n",
    "df, df_pred = get_statewise_testing_data()\n",
    "\n",
    "# Initialize the model:\n",
    "mod = case_count_model_us_states(df)\n",
    "\n",
    "# Run MCMC sampler\n",
    "with mod:\n",
    "    trace = pm.sample(500, tune=500, chains=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-10T00:17:53.894370Z",
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     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Summary for the United States on 2020-04-10:"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Reported Case Count:**  457,778"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Predicted Case Count:**  577,459"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "**Percentage Underreporting in Case Count:**  20.7%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_input\n",
    "\n",
    "n = len(trace['beta'])\n",
    "\n",
    "# South Korea:\n",
    "ppt_sk = np.log10(51500000. / 250000)\n",
    "\n",
    "\n",
    "# Compute predicted case counts per state right now\n",
    "logit_now = pd.DataFrame([\n",
    "    pd.Series(np.random.normal((trace['alpha'][i] + trace['beta'][i] * (np.log10(df_pred['people_per_test']) - ppt_sk)),\n",
    "                     trace['sigma'][i]), index=df_pred.index)\n",
    "    for i in range(len(trace['beta']))])\n",
    "prob_missing_now = np.exp(logit_now) / (np.exp(logit_now) + 1) \n",
    "\n",
    "predicted_counts_now = np.round(df_pred['positive'] / prob_missing_now.mean(axis=0)).astype(int)\n",
    "\n",
    "predicted_counts_now_lower = np.round(df_pred['positive'] / prob_missing_now.quantile(0.975, axis=0)).astype(int)\n",
    "predicted_counts_now_upper = np.round(df_pred['positive'] / prob_missing_now.quantile(0.025, axis=0)).astype(int)\n",
    "\n",
    "case_increase_percent = list(map(lambda x, y: (((x - y) / float(y))),\n",
    "                                 predicted_counts_now, df_pred['positive']))\n",
    "\n",
    "df_summary = pd.DataFrame(\n",
    "    data = {\n",
    "     'Cases Reported': df_pred['positive'],\n",
    "     'Cases Estimated': predicted_counts_now,\n",
    "     'Percent Increase': case_increase_percent,\n",
    "     'Tests per Million People': df_pred['tests_per_million'].round(1),\n",
    "     'Cases Estimated (range)': list(map(lambda x, y: '(%i, %i)' % (round(x), round(y)),\n",
    "                                        predicted_counts_now_lower, predicted_counts_now_upper)),\n",
    "     'Cases per Million': ((df_pred['positive'] / df_pred['total_population']) * 1e6),\n",
    "     'Positive Test Rate': (df_pred['positive'] / (df_pred['positive'] + df_pred['negative']))\n",
    "    },\n",
    "    index=df_pred.index)\n",
    "\n",
    "from datetime import datetime\n",
    "display(Markdown(\"## Summary for the United States on %s:\" % str(datetime.today())[:10]))\n",
    "display(Markdown(f\"**Reported Case Count:**  {df_summary['Cases Reported'].sum():,}\"))\n",
    "display(Markdown(f\"**Predicted Case Count:**  {df_summary['Cases Estimated'].sum():,}\"))\n",
    "case_increase_percent = 100. * (df_summary['Cases Estimated'].sum() - df_summary['Cases Reported'].sum()) / df_summary['Cases Estimated'].sum()\n",
    "display(Markdown(\"**Percentage Underreporting in Case Count:**  %.1f%%\" % case_increase_percent))"
   ]
  },
  {
   "cell_type": "code",
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    "execution": {
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   "source": [
    "#hide\n",
    "df_summary.loc[:, 'Ratio'] = df_summary['Cases Estimated'] / df_summary['Cases Reported']\n",
    "df_summary.columns = ['Reported Cases', 'Est Cases', '% Increase',\n",
    "                      'Tests per Million', 'Est Range',\n",
    "                      'Cases per Million', 'Positive Test Rate',\n",
    "                      'Ratio']\n",
    "\n",
    "df_display = df_summary[['Reported Cases', 'Est Cases', 'Est Range', 'Ratio',\n",
    "                         'Tests per Million', 'Cases per Million',\n",
    "                         'Positive Test Rate']].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "## COVID-19 Case Estimates, by State\n",
    "\n",
    "### Definition Of Fields:\n",
    "\n",
    "- **Reported Cases**:  The number of cases reported by each state, which is a function of how many tests are positive.\n",
    "- **Est Cases**: The predicted number of cases, accounting for the fact that not everyone is tested.\n",
    "- **Est Range**: The 95% confidence interval of the predicted number of cases.\n",
    "- **Ratio**: `Estimated Cases` divided by `Reported Cases`.\n",
    "- **Tests per Million**:  The number of tests administered per one million people.  The less tests administered per capita, the larger the difference between reported and estimated number of cases, generally.\n",
    "- **Cases per Million**: The number of **reported** cases per on million people.\n",
    "- **Positive Test Rate**: The **reported** percentage of positive tests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2020-04-10T00:17:55.886151Z"
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       "        }</style><table id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108be\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Reported Cases</th>        <th class=\"col_heading level0 col1\" >Est Cases</th>        <th class=\"col_heading level0 col2\" >Est Range</th>        <th class=\"col_heading level0 col3\" >Ratio</th>        <th class=\"col_heading level0 col4\" >Tests per Million</th>        <th class=\"col_heading level0 col5\" >Cases per Million</th>        <th class=\"col_heading level0 col6\" >Positive Test Rate</th>    </tr>    <tr>        <th class=\"index_name level0\" >state</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row0\" class=\"row_heading level0 row0\" >NY</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow0_col0\" class=\"data row0 col0\" >159937</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow0_col1\" class=\"data row0 col1\" >191059</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow0_col2\" class=\"data row0 col2\" >(168029, 238572)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow0_col3\" class=\"data row0 col3\" >1.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow0_col4\" class=\"data row0 col4\" >20127.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow0_col5\" class=\"data row0 col5\" >8221.5</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow0_col6\" class=\"data row0 col6\" >41%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row1\" class=\"row_heading level0 row1\" >NJ</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow1_col0\" class=\"data row1 col0\" >51027</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow1_col1\" class=\"data row1 col1\" >63095</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow1_col2\" class=\"data row1 col2\" >(54322, 87701)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow1_col3\" class=\"data row1 col3\" >1.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow1_col4\" class=\"data row1 col4\" >12068.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow1_col5\" class=\"data row1 col5\" >5744.9</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow1_col6\" class=\"data row1 col6\" >48%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row2\" class=\"row_heading level0 row2\" >MI</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow2_col0\" class=\"data row2 col0\" >21504</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow2_col1\" class=\"data row2 col1\" >28986</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow2_col2\" class=\"data row2 col2\" >(23552, 46553)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow2_col3\" class=\"data row2 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow2_col4\" class=\"data row2 col4\" >5293.6</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow2_col5\" class=\"data row2 col5\" >2153.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow2_col6\" class=\"data row2 col6\" >41%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row3\" class=\"row_heading level0 row3\" >CA</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow3_col0\" class=\"data row3 col0\" >18309</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow3_col1\" class=\"data row3 col1\" >25335</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow3_col2\" class=\"data row3 col2\" >(20174, 41907)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow3_col3\" class=\"data row3 col3\" >1.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow3_col4\" class=\"data row3 col4\" >4138.0</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow3_col5\" class=\"data row3 col5\" >463.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow3_col6\" class=\"data row3 col6\" >11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row4\" class=\"row_heading level0 row4\" >MA</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow4_col0\" class=\"data row4 col0\" >18941</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow4_col1\" class=\"data row4 col1\" >23394</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow4_col2\" class=\"data row4 col2\" >(20117, 31629)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow4_col3\" class=\"data row4 col3\" >1.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow4_col4\" class=\"data row4 col4\" >13777.0</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow4_col5\" class=\"data row4 col5\" >2748.1</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow4_col6\" class=\"data row4 col6\" >20%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row5\" class=\"row_heading level0 row5\" >PA</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow5_col0\" class=\"data row5 col0\" >18228</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow5_col1\" class=\"data row5 col1\" >23380</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow5_col2\" class=\"data row5 col2\" >(19623, 33993)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow5_col3\" class=\"data row5 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow5_col4\" class=\"data row5 col4\" >8248.9</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow5_col5\" class=\"data row5 col5\" >1423.8</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow5_col6\" class=\"data row5 col6\" >17%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row6\" class=\"row_heading level0 row6\" >LA</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow6_col0\" class=\"data row6 col0\" >18283</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow6_col1\" class=\"data row6 col1\" >21883</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow6_col2\" class=\"data row6 col2\" >(19276, 28111)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow6_col3\" class=\"data row6 col3\" >1.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow6_col4\" class=\"data row6 col4\" >18697.1</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow6_col5\" class=\"data row6 col5\" >3932.8</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow6_col6\" class=\"data row6 col6\" >21%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row7\" class=\"row_heading level0 row7\" >IL</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow7_col0\" class=\"data row7 col0\" >16422</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow7_col1\" class=\"data row7 col1\" >21614</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow7_col2\" class=\"data row7 col2\" >(17842, 33187)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow7_col3\" class=\"data row7 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow7_col4\" class=\"data row7 col4\" >6380.9</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow7_col5\" class=\"data row7 col5\" >1295.9</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow7_col6\" class=\"data row7 col6\" >20%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row8\" class=\"row_heading level0 row8\" >FL</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow8_col0\" class=\"data row8 col0\" >16364</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow8_col1\" class=\"data row8 col1\" >21462</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow8_col2\" class=\"data row8 col2\" >(17616, 32651)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow8_col3\" class=\"data row8 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow8_col4\" class=\"data row8 col4\" >7102.7</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow8_col5\" class=\"data row8 col5\" >761.9</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow8_col6\" class=\"data row8 col6\" >11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row9\" class=\"row_heading level0 row9\" >GA</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow9_col0\" class=\"data row9 col0\" >10566</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow9_col1\" class=\"data row9 col1\" >14526</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow9_col2\" class=\"data row9 col2\" >(11535, 22388)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow9_col3\" class=\"data row9 col3\" >1.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow9_col4\" class=\"data row9 col4\" >3869.6</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow9_col5\" class=\"data row9 col5\" >995.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow9_col6\" class=\"data row9 col6\" >26%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row10\" class=\"row_heading level0 row10\" >TX</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow10_col0\" class=\"data row10 col0\" >10230</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow10_col1\" class=\"data row10 col1\" >14409</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow10_col2\" class=\"data row10 col2\" >(11315, 23490)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow10_col3\" class=\"data row10 col3\" >1.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow10_col4\" class=\"data row10 col4\" >3660.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow10_col5\" class=\"data row10 col5\" >352.8</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow10_col6\" class=\"data row10 col6\" >10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row11\" class=\"row_heading level0 row11\" >CT</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow11_col0\" class=\"data row11 col0\" >9784</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow11_col1\" class=\"data row11 col1\" >12255</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow11_col2\" class=\"data row11 col2\" >(10554, 16942)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow11_col3\" class=\"data row11 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow11_col4\" class=\"data row11 col4\" >9396.7</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow11_col5\" class=\"data row11 col5\" >2744.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow11_col6\" class=\"data row11 col6\" >29%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row12\" class=\"row_heading level0 row12\" >WA</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow12_col0\" class=\"data row12 col0\" >9097</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow12_col1\" class=\"data row12 col1\" >11233</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow12_col2\" class=\"data row12 col2\" >(9656, 14616)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow12_col3\" class=\"data row12 col3\" >1.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow12_col4\" class=\"data row12 col4\" >12145.7</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow12_col5\" class=\"data row12 col5\" >1194.6</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow12_col6\" class=\"data row12 col6\" >10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row13\" class=\"row_heading level0 row13\" >IN</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow13_col0\" class=\"data row13 col0\" >6351</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow13_col1\" class=\"data row13 col1\" >8446</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow13_col2\" class=\"data row13 col2\" >(6917, 12362)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow13_col3\" class=\"data row13 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow13_col4\" class=\"data row13 col4\" >4773.0</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow13_col5\" class=\"data row13 col5\" >943.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow13_col6\" class=\"data row13 col6\" >20%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row14\" class=\"row_heading level0 row14\" >MD</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow14_col0\" class=\"data row14 col0\" >6185</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow14_col1\" class=\"data row14 col1\" >8045</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow14_col2\" class=\"data row14 col2\" >(6672, 11683)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow14_col3\" class=\"data row14 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow14_col4\" class=\"data row14 col4\" >6869.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow14_col5\" class=\"data row14 col5\" >1023.0</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow14_col6\" class=\"data row14 col6\" >15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row15\" class=\"row_heading level0 row15\" >CO</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow15_col0\" class=\"data row15 col0\" >5655</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow15_col1\" class=\"data row15 col1\" >7734</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow15_col2\" class=\"data row15 col2\" >(6174, 12900)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow15_col3\" class=\"data row15 col3\" >1.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow15_col4\" class=\"data row15 col4\" >5070.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow15_col5\" class=\"data row15 col5\" >982.0</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow15_col6\" class=\"data row15 col6\" >19%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row16\" class=\"row_heading level0 row16\" >OH</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow16_col0\" class=\"data row16 col0\" >5512</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow16_col1\" class=\"data row16 col1\" >7476</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow16_col2\" class=\"data row16 col2\" >(6044, 12183)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow16_col3\" class=\"data row16 col3\" >1.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow16_col4\" class=\"data row16 col4\" >4789.5</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow16_col5\" class=\"data row16 col5\" >471.6</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow16_col6\" class=\"data row16 col6\" >10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row17\" class=\"row_heading level0 row17\" >TN</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow17_col0\" class=\"data row17 col0\" >4634</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow17_col1\" class=\"data row17 col1\" >5906</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow17_col2\" class=\"data row17 col2\" >(5014, 8363)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow17_col3\" class=\"data row17 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow17_col4\" class=\"data row17 col4\" >8763.7</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow17_col5\" class=\"data row17 col5\" >678.6</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow17_col6\" class=\"data row17 col6\" >8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row18\" class=\"row_heading level0 row18\" >VA</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow18_col0\" class=\"data row18 col0\" >4042</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow18_col1\" class=\"data row18 col1\" >5566</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow18_col2\" class=\"data row18 col2\" >(4419, 9143)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow18_col3\" class=\"data row18 col3\" >1.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow18_col4\" class=\"data row18 col4\" >3869.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow18_col5\" class=\"data row18 col5\" >473.6</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow18_col6\" class=\"data row18 col6\" >12%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row19\" class=\"row_heading level0 row19\" >NC</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow19_col0\" class=\"data row19 col0\" >3651</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow19_col1\" class=\"data row19 col1\" >4967</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow19_col2\" class=\"data row19 col2\" >(3995, 7841)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow19_col3\" class=\"data row19 col3\" >1.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow19_col4\" class=\"data row19 col4\" >4558.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow19_col5\" class=\"data row19 col5\" >348.1</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow19_col6\" class=\"data row19 col6\" >8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row20\" class=\"row_heading level0 row20\" >MO</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow20_col0\" class=\"data row20 col0\" >3539</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow20_col1\" class=\"data row20 col1\" >4637</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow20_col2\" class=\"data row20 col2\" >(3833, 6967)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow20_col3\" class=\"data row20 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow20_col4\" class=\"data row20 col4\" >6272.0</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow20_col5\" class=\"data row20 col5\" >576.6</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow20_col6\" class=\"data row20 col6\" >9%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row21\" class=\"row_heading level0 row21\" >AZ</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow21_col0\" class=\"data row21 col0\" >3018</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow21_col1\" class=\"data row21 col1\" >4038</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow21_col2\" class=\"data row21 col2\" >(3308, 5953)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow21_col3\" class=\"data row21 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow21_col4\" class=\"data row21 col4\" >5107.8</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow21_col5\" class=\"data row21 col5\" >414.6</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow21_col6\" class=\"data row21 col6\" >8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row22\" class=\"row_heading level0 row22\" >WI</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow22_col0\" class=\"data row22 col0\" >2885</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow22_col1\" class=\"data row22 col1\" >3822</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow22_col2\" class=\"data row22 col2\" >(3138, 5635)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow22_col3\" class=\"data row22 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow22_col4\" class=\"data row22 col4\" >5892.6</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow22_col5\" class=\"data row22 col5\" >495.5</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow22_col6\" class=\"data row22 col6\" >8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row23\" class=\"row_heading level0 row23\" >AL</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow23_col0\" class=\"data row23 col0\" >2769</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow23_col1\" class=\"data row23 col1\" >3785</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow23_col2\" class=\"data row23 col2\" >(3016, 6194)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow23_col3\" class=\"data row23 col3\" >1.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow23_col4\" class=\"data row23 col4\" >4247.6</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow23_col5\" class=\"data row23 col5\" >564.7</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow23_col6\" class=\"data row23 col6\" >13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row24\" class=\"row_heading level0 row24\" >SC</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow24_col0\" class=\"data row24 col0\" >2792</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow24_col1\" class=\"data row24 col1\" >3751</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow24_col2\" class=\"data row24 col2\" >(3052, 5654)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow24_col3\" class=\"data row24 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow24_col4\" class=\"data row24 col4\" >5315.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow24_col5\" class=\"data row24 col5\" >542.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow24_col6\" class=\"data row24 col6\" >10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row25\" class=\"row_heading level0 row25\" >NV</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow25_col0\" class=\"data row25 col0\" >2456</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow25_col1\" class=\"data row25 col1\" >3203</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow25_col2\" class=\"data row25 col2\" >(2672, 4683)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow25_col3\" class=\"data row25 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow25_col4\" class=\"data row25 col4\" >7068.1</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow25_col5\" class=\"data row25 col5\" >797.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow25_col6\" class=\"data row25 col6\" >11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row26\" class=\"row_heading level0 row26\" >MS</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow26_col0\" class=\"data row26 col0\" >2260</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow26_col1\" class=\"data row26 col1\" >2923</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow26_col2\" class=\"data row26 col2\" >(2446, 4275)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow26_col3\" class=\"data row26 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow26_col4\" class=\"data row26 col4\" >7019.8</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow26_col5\" class=\"data row26 col5\" >759.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow26_col6\" class=\"data row26 col6\" >11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row27\" class=\"row_heading level0 row27\" >UT</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow27_col0\" class=\"data row27 col0\" >1976</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow27_col1\" class=\"data row27 col1\" >2451</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow27_col2\" class=\"data row27 col2\" >(2111, 3483)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow27_col3\" class=\"data row27 col3\" >1.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow27_col4\" class=\"data row27 col4\" >11969.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow27_col5\" class=\"data row27 col5\" >616.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow27_col6\" class=\"data row27 col6\" >5%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row28\" class=\"row_heading level0 row28\" >OK</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow28_col0\" class=\"data row28 col0\" >1684</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow28_col1\" class=\"data row28 col1\" >2269</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow28_col2\" class=\"data row28 col2\" >(1833, 3436)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow28_col3\" class=\"data row28 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow28_col4\" class=\"data row28 col4\" >5124.9</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow28_col5\" class=\"data row28 col5\" >425.6</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow28_col6\" class=\"data row28 col6\" >8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row29\" class=\"row_heading level0 row29\" >RI</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow29_col0\" class=\"data row29 col0\" >1727</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow29_col1\" class=\"data row29 col1\" >2162</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow29_col2\" class=\"data row29 col2\" >(1856, 3002)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow29_col3\" class=\"data row29 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow29_col4\" class=\"data row29 col4\" >11713.7</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow29_col5\" class=\"data row29 col5\" >1630.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow29_col6\" class=\"data row29 col6\" >14%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row30\" class=\"row_heading level0 row30\" >DC</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow30_col0\" class=\"data row30 col0\" >1523</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow30_col1\" class=\"data row30 col1\" >1886</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow30_col2\" class=\"data row30 col2\" >(1636, 2536)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow30_col3\" class=\"data row30 col3\" >1.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow30_col4\" class=\"data row30 col4\" >12361.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow30_col5\" class=\"data row30 col5\" >2158.0</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow30_col6\" class=\"data row30 col6\" >17%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row31\" class=\"row_heading level0 row31\" >KY</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow31_col0\" class=\"data row31 col0\" >1346</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow31_col1\" class=\"data row31 col1\" >1817</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow31_col2\" class=\"data row31 col2\" >(1478, 2712)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow31_col3\" class=\"data row31 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow31_col4\" class=\"data row31 col4\" >4879.7</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow31_col5\" class=\"data row31 col5\" >301.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow31_col6\" class=\"data row31 col6\" >6%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row32\" class=\"row_heading level0 row32\" >OR</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow32_col0\" class=\"data row32 col0\" >1321</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow32_col1\" class=\"data row32 col1\" >1740</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow32_col2\" class=\"data row32 col2\" >(1440, 2671)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow32_col3\" class=\"data row32 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow32_col4\" class=\"data row32 col4\" >6076.0</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow32_col5\" class=\"data row32 col5\" >313.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow32_col6\" class=\"data row32 col6\" >5%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row33\" class=\"row_heading level0 row33\" >IA</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow33_col0\" class=\"data row33 col0\" >1270</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow33_col1\" class=\"data row33 col1\" >1716</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow33_col2\" class=\"data row33 col2\" >(1386, 2519)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow33_col3\" class=\"data row33 col3\" >1.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow33_col4\" class=\"data row33 col4\" >4745.7</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow33_col5\" class=\"data row33 col5\" >402.5</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow33_col6\" class=\"data row33 col6\" >8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row34\" class=\"row_heading level0 row34\" >MN</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow34_col0\" class=\"data row34 col0\" >1242</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow34_col1\" class=\"data row34 col1\" >1643</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow34_col2\" class=\"data row34 col2\" >(1359, 2450)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow34_col3\" class=\"data row34 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow34_col4\" class=\"data row34 col4\" >5726.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow34_col5\" class=\"data row34 col5\" >220.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow34_col6\" class=\"data row34 col6\" >4%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row35\" class=\"row_heading level0 row35\" >ID</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow35_col0\" class=\"data row35 col0\" >1232</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow35_col1\" class=\"data row35 col1\" >1610</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow35_col2\" class=\"data row35 col2\" >(1325, 2374)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow35_col3\" class=\"data row35 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow35_col4\" class=\"data row35 col4\" >7012.1</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow35_col5\" class=\"data row35 col5\" >689.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow35_col6\" class=\"data row35 col6\" >10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row36\" class=\"row_heading level0 row36\" >KS</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow36_col0\" class=\"data row36 col0\" >1106</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow36_col1\" class=\"data row36 col1\" >1553</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow36_col2\" class=\"data row36 col2\" >(1225, 2491)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow36_col3\" class=\"data row36 col3\" >1.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow36_col4\" class=\"data row36 col4\" >3698.5</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow36_col5\" class=\"data row36 col5\" >379.6</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow36_col6\" class=\"data row36 col6\" >10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row37\" class=\"row_heading level0 row37\" >DE</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow37_col0\" class=\"data row37 col0\" >1207</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow37_col1\" class=\"data row37 col1\" >1523</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow37_col2\" class=\"data row37 col2\" >(1285, 2225)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow37_col3\" class=\"data row37 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow37_col4\" class=\"data row37 col4\" >10156.5</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow37_col5\" class=\"data row37 col5\" >1239.5</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow37_col6\" class=\"data row37 col6\" >12%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row38\" class=\"row_heading level0 row38\" >AR</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow38_col0\" class=\"data row38 col0\" >1119</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow38_col1\" class=\"data row38 col1\" >1512</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow38_col2\" class=\"data row38 col2\" >(1220, 2403)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow38_col3\" class=\"data row38 col3\" >1.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow38_col4\" class=\"data row38 col4\" >4954.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow38_col5\" class=\"data row38 col5\" >370.8</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow38_col6\" class=\"data row38 col6\" >7%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row39\" class=\"row_heading level0 row39\" >NM</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow39_col0\" class=\"data row39 col0\" >865</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow39_col1\" class=\"data row39 col1\" >1083</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow39_col2\" class=\"data row39 col2\" >(916, 1528)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow39_col3\" class=\"data row39 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow39_col4\" class=\"data row39 col4\" >11353.8</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow39_col5\" class=\"data row39 col5\" >412.5</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow39_col6\" class=\"data row39 col6\" >4%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row40\" class=\"row_heading level0 row40\" >PR</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow40_col0\" class=\"data row40 col0\" >683</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow40_col1\" class=\"data row40 col1\" >1057</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow40_col2\" class=\"data row40 col2\" >(782, 1854)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow40_col3\" class=\"data row40 col3\" >1.5</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow40_col4\" class=\"data row40 col4\" >1686.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow40_col5\" class=\"data row40 col5\" >213.9</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow40_col6\" class=\"data row40 col6\" >13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row41\" class=\"row_heading level0 row41\" >NH</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow41_col0\" class=\"data row41 col0\" >788</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow41_col1\" class=\"data row41 col1\" >1015</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow41_col2\" class=\"data row41 col2\" >(854, 1454)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow41_col3\" class=\"data row41 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow41_col4\" class=\"data row41 col4\" >7024.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow41_col5\" class=\"data row41 col5\" >579.5</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow41_col6\" class=\"data row41 col6\" >8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row42\" class=\"row_heading level0 row42\" >NE</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow42_col0\" class=\"data row42 col0\" >567</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow42_col1\" class=\"data row42 col1\" >775</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow42_col2\" class=\"data row42 col2\" >(622, 1220)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow42_col3\" class=\"data row42 col3\" >1.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow42_col4\" class=\"data row42 col4\" >4488.7</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow42_col5\" class=\"data row42 col5\" >293.1</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow42_col6\" class=\"data row42 col6\" >7%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row43\" class=\"row_heading level0 row43\" >VT</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow43_col0\" class=\"data row43 col0\" >628</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow43_col1\" class=\"data row43 col1\" >775</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow43_col2\" class=\"data row43 col2\" >(662, 1042)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow43_col3\" class=\"data row43 col3\" >1.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow43_col4\" class=\"data row43 col4\" >13110.8</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow43_col5\" class=\"data row43 col5\" >1006.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow43_col6\" class=\"data row43 col6\" >8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row44\" class=\"row_heading level0 row44\" >ME</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow44_col0\" class=\"data row44 col0\" >560</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow44_col1\" class=\"data row44 col1\" >757</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow44_col2\" class=\"data row44 col2\" >(619, 1201)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow44_col3\" class=\"data row44 col3\" >1.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow44_col4\" class=\"data row44 col4\" >4945.6</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow44_col5\" class=\"data row44 col5\" >416.6</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow44_col6\" class=\"data row44 col6\" >8%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row45\" class=\"row_heading level0 row45\" >WV</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow45_col0\" class=\"data row45 col0\" >523</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow45_col1\" class=\"data row45 col1\" >674</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow45_col2\" class=\"data row45 col2\" >(561, 1024)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow45_col3\" class=\"data row45 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow45_col4\" class=\"data row45 col4\" >7735.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow45_col5\" class=\"data row45 col5\" >291.8</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow45_col6\" class=\"data row45 col6\" >4%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row46\" class=\"row_heading level0 row46\" >SD</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow46_col0\" class=\"data row46 col0\" >447</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow46_col1\" class=\"data row46 col1\" >574</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow46_col2\" class=\"data row46 col2\" >(485, 826)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow46_col3\" class=\"data row46 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow46_col4\" class=\"data row46 col4\" >8078.8</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow46_col5\" class=\"data row46 col5\" >505.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow46_col6\" class=\"data row46 col6\" >6%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row47\" class=\"row_heading level0 row47\" >HI</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow47_col0\" class=\"data row47 col0\" >435</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow47_col1\" class=\"data row47 col1\" >546</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow47_col2\" class=\"data row47 col2\" >(465, 761)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow47_col3\" class=\"data row47 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow47_col4\" class=\"data row47 col4\" >11124.6</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow47_col5\" class=\"data row47 col5\" >307.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow47_col6\" class=\"data row47 col6\" >3%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row48\" class=\"row_heading level0 row48\" >MT</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow48_col0\" class=\"data row48 col0\" >354</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow48_col1\" class=\"data row48 col1\" >462</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow48_col2\" class=\"data row48 col2\" >(385, 650)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow48_col3\" class=\"data row48 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow48_col4\" class=\"data row48 col4\" >7354.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow48_col5\" class=\"data row48 col5\" >331.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow48_col6\" class=\"data row48 col6\" >5%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row49\" class=\"row_heading level0 row49\" >ND</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow49_col0\" class=\"data row49 col0\" >269</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow49_col1\" class=\"data row49 col1\" >335</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow49_col2\" class=\"data row49 col2\" >(286, 475)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow49_col3\" class=\"data row49 col3\" >1.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow49_col4\" class=\"data row49 col4\" >11796.9</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow49_col5\" class=\"data row49 col5\" >353.0</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow49_col6\" class=\"data row49 col6\" >3%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row50\" class=\"row_heading level0 row50\" >WY</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow50_col0\" class=\"data row50 col0\" >230</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow50_col1\" class=\"data row50 col1\" >297</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow50_col2\" class=\"data row50 col2\" >(250, 441)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow50_col3\" class=\"data row50 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow50_col4\" class=\"data row50 col4\" >7170.5</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow50_col5\" class=\"data row50 col5\" >397.4</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow50_col6\" class=\"data row50 col6\" >6%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108belevel0_row51\" class=\"row_heading level0 row51\" >AK</th>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow51_col0\" class=\"data row51 col0\" >235</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow51_col1\" class=\"data row51 col1\" >297</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow51_col2\" class=\"data row51 col2\" >(252, 405)</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow51_col3\" class=\"data row51 col3\" >1.3</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow51_col4\" class=\"data row51 col4\" >9873.6</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow51_col5\" class=\"data row51 col5\" >321.2</td>\n",
       "                        <td id=\"T_b9d9a90a_7ac0_11ea_b595_000d3a7108berow51_col6\" class=\"data row51 col6\" >3%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f272683a470>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "df_display.sort_values(\n",
    "    by='Est Cases', ascending=False).style.background_gradient(\n",
    "    cmap='Oranges').format(\n",
    "    {'Ratio': \"{:.1f}\"}).format(\n",
    "    {'Tests per Million': \"{:.1f}\"}).format(\n",
    "    {'Cases per Million': \"{:.1f}\"}).format(\n",
    "    {'Positive Test Rate': \"{:.0%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-10T00:17:56.370742Z",
     "iopub.status.busy": "2020-04-10T00:17:56.367660Z",
     "iopub.status.idle": "2020-04-10T00:17:56.870921Z",
     "shell.execute_reply": "2020-04-10T00:17:56.871344Z"
    },
    "papermill": {
     "duration": 0.748439,
     "end_time": "2020-04-10T00:17:56.871481",
     "exception": false,
     "start_time": "2020-04-10T00:17:56.123042",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_input\n",
    "\n",
    "df_plot = df_summary.copy(deep=True)\n",
    "\n",
    "# Compute predicted cases per million\n",
    "df_plot['predicted_counts_now_pm'] =  1e6 * (\n",
    "    df_pred['positive'] / prob_missing_now.mean(axis=0)) / df_pred['total_population']\n",
    "\n",
    "df_plot['predicted_counts_now_lower_pm'] = 1e6 * (\n",
    "    df_pred['positive'] / prob_missing_now.quantile(0.975, axis=0))/ df_pred['total_population']\n",
    "df_plot['predicted_counts_now_upper_pm'] = 1e6 * (\n",
    "    df_pred['positive'] / prob_missing_now.quantile(0.025, axis=0))/ df_pred['total_population']\n",
    "\n",
    "df_plot.sort_values('predicted_counts_now_pm', ascending=False, inplace=True)\n",
    "\n",
    "xerr = [\n",
    "    df_plot['predicted_counts_now_pm'] - df_plot['predicted_counts_now_lower_pm'], \n",
    "    df_plot['predicted_counts_now_upper_pm'] - df_plot['predicted_counts_now_pm']]\n",
    "\n",
    "fig, axs = plt.subplots(1, 1, figsize=(15, 15))\n",
    "ax = plt.errorbar(df_plot['predicted_counts_now_pm'], range(len(df_plot)-1, -1, -1),\n",
    "                  xerr=xerr, fmt='o', elinewidth=1, label='Estimate')\n",
    "ax = plt.yticks(range(len(df_plot)), df_plot.index[::-1])\n",
    "ax = plt.errorbar(df_plot['Cases per Million'], range(len(df_plot)-1, -1, -1),\n",
    "                  xerr=None, fmt='.', color='k', label='Reported')\n",
    "ax = plt.xlabel('COVID-19 Case Counts Per Million People', size=20)\n",
    "ax = plt.legend(fontsize='xx-large', loc=4)\n",
    "ax = plt.grid(linestyle='--', color='grey', axis='x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.241692,
     "end_time": "2020-04-10T00:17:57.354441",
     "exception": false,
     "start_time": "2020-04-10T00:17:57.112749",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Appendix: Model Diagnostics \n",
    "\n",
    "### Derived relationship between Test Capacity and Case Under-reporting\n",
    "\n",
    "Plotted is the estimated relationship between test capacity (in terms of people per test -- larger = less testing) and the likelihood a COVID-19 case is reported (lower = more under-reporting of cases).\n",
    "\n",
    "The lines represent the posterior samples from our MCMC run (note the x-axis is plotted on a log scale).  The rug plot shows the current test capacity for each state (black '|') and the capacity one week ago (cyan '+').  For comparison, South Korea's testing capacity is currently at the very left of the graph (200 people per test)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-10T00:17:57.860723Z",
     "iopub.status.busy": "2020-04-10T00:17:57.857916Z",
     "iopub.status.idle": "2020-04-10T00:17:59.849706Z",
     "shell.execute_reply": "2020-04-10T00:17:59.850483Z"
    },
    "papermill": {
     "duration": 2.252259,
     "end_time": "2020-04-10T00:17:59.850602",
     "exception": false,
     "start_time": "2020-04-10T00:17:57.598343",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_input\n",
    "\n",
    "# Plot pop/test vs. Prob of case detection for all posterior samples:\n",
    "x = np.linspace(0.0, 4.0, 101)\n",
    "logit_pcase = pd.DataFrame([\n",
    "    trace['alpha'][i] + trace['beta'][i] * x\n",
    "    for i in range(n)])\n",
    "pcase = np.exp(logit_pcase) / (np.exp(logit_pcase) + 1)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(14, 9))\n",
    "for i in range(n):\n",
    "    ax = plt.plot(10**(ppt_sk + x), pcase.iloc[i], color='grey', lw=.1, alpha=.5)\n",
    "    plt.xscale('log')\n",
    "    plt.xlabel('State-wise population per test', size=14)\n",
    "    plt.ylabel('Probability a true case is detected', size=14)\n",
    "\n",
    "# rug plots:\n",
    "ax=plt.plot(df_pred['people_per_test'], np.zeros(len(df_pred)),\n",
    "            marker='|', color='k', ls='', ms=20,\n",
    "           label='U.S. State-wise Test Capacity Now')\n",
    "ax=plt.plot(df['people_per_test_7_days_ago'], np.zeros(len(df)),\n",
    "            marker='+', color='c', ls='', ms=10,\n",
    "           label='U.S. State-wise Test Capacity 7 Days Ago')\n",
    "ax = plt.legend(fontsize='x-large')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.234009,
     "end_time": "2020-04-10T00:18:00.312820",
     "exception": false,
     "start_time": "2020-04-10T00:18:00.078811",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## About this Analysis\n",
    "\n",
    "This analysis was done by [Joseph Richards](https://twitter.com/joeyrichar).\n",
    "\n",
    "This project[^1] uses the testing rates per state from [https://covidtracking.com/](https://covidtracking.com/), which reports case counts and mortality by state.  This is used to **estimate the number of unreported (untested) COVID-19 cases in each U.S. state.**\n",
    "\n",
    "The analysis makes a few assumptions:\n",
    "\n",
    "1. The probability that a case is reported by a state is a function of the number of tests run per person in that state.  Hence the degree of under-reported cases is a function of tests run per capita.\n",
    "2. The underlying mortality rate is the same across every state.\n",
    "3. Patients take time to succumb to COVID-19, so the mortality counts *today* reflect the case counts *7 days ago*.  E.g., mortality rate = (cumulative deaths today) / (cumulative cases 7 days ago).\n",
    "\n",
    "The model attempts to find the most likely relationship between state-wise test volume (per capita) and under-reporting, such that the true underlying mortality rates between the individual states are as similar as possible.  The model simultaneously finds the most likely posterior distribution of mortality rates, the most likely *true* case count per state, and the test volume vs. case underreporting relationship.\n",
    "\n",
    "[^1]: Full details about the model are available at:  https://github.com/jwrichar/COVID19-mortality"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "visualization-curriculum-gF8wUgMm",
   "language": "python",
   "name": "visualization-curriculum-gf8wugmm"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  },
  "papermill": {
   "duration": 254.576234,
   "end_time": "2020-04-10T00:18:00.651099",
   "environment_variables": {},
   "exception": null,
   "input_path": "2020-03-18-case-count-estimation-us-states.ipynb",
   "output_path": "2020-03-18-case-count-estimation-us-states.ipynb",
   "parameters": {},
   "start_time": "2020-04-10T00:13:46.074865",
   "version": "2.0.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}